Overview

Dataset statistics

Number of variables11
Number of observations10437
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory138.0 B

Variable types

Numeric10
Categorical1

Alerts

modular_ratio is highly correlated with ratioHigh correlation
weight is highly correlated with peak_numberHigh correlation
peak_number is highly correlated with weightHigh correlation
ratio is highly correlated with modular_ratioHigh correlation
modular_ratio is highly correlated with ratioHigh correlation
weight is highly correlated with peak_numberHigh correlation
peak_number is highly correlated with weightHigh correlation
ratio is highly correlated with modular_ratioHigh correlation
modular_ratio is highly correlated with ratioHigh correlation
ratio is highly correlated with modular_ratioHigh correlation
intercolumnar_distance is highly correlated with row_numberHigh correlation
upper_margin is highly correlated with row_number and 1 other fieldsHigh correlation
lower_margin is highly correlated with row_numberHigh correlation
row_number is highly correlated with intercolumnar_distance and 5 other fieldsHigh correlation
modular_ratio is highly correlated with ratioHigh correlation
interlinear_spacing is highly correlated with row_number and 1 other fieldsHigh correlation
weight is highly correlated with peak_numberHigh correlation
peak_number is highly correlated with row_number and 2 other fieldsHigh correlation
ratio is highly correlated with modular_ratio and 1 other fieldsHigh correlation
class is highly correlated with upper_margin and 2 other fieldsHigh correlation

Reproduction

Analysis started2022-09-03 01:21:35.013007
Analysis finished2022-09-03 01:21:53.504450
Duration18.49 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

intercolumnar_distance
Real number (ℝ)

HIGH CORRELATION

Distinct143
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.0008518787966
Minimum-3.498799
Maximum11.819916
Zeros0
Zeros (%)0.0%
Negative4330
Negative (%)41.5%
Memory size81.7 KiB
2022-09-02T21:21:53.677448image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-3.498799
5-th percentile-0.597995
Q1-0.128929
median0.056229
Q30.204355
95-th percentile0.698109
Maximum11.819916
Range15.318715
Interquartile range (IQR)0.333284

Descriptive statistics

Standard deviation1.008551356
Coefficient of variation (CV)-1183.914143
Kurtosis40.06528989
Mean-0.0008518787966
Median Absolute Deviation (MAD)0.172814
Skewness2.561404913
Sum-8.891059
Variance1.017175837
MonotonicityNot monotonic
2022-09-02T21:21:53.917450image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-3.498799315
 
3.0%
0.080916311
 
3.0%
0.15498292
 
2.8%
0.019197277
 
2.7%
-0.042522250
 
2.4%
0.117948247
 
2.4%
0.130292244
 
2.3%
-0.128929235
 
2.3%
0.068573233
 
2.2%
0.142636228
 
2.2%
Other values (133)7805
74.8%
ValueCountFrequency (%)
-3.498799315
3.0%
-3.4864558
 
0.1%
-3.4617684
 
< 0.1%
-3.437084
 
< 0.1%
-3.41239211
 
0.1%
-3.05442110
 
0.1%
-2.8075449
 
0.1%
-2.57301112
 
0.1%
-2.52363512
 
0.1%
-2.474266
 
0.1%
ValueCountFrequency (%)
11.8199168
0.1%
9.94365113
0.1%
9.523965
 
< 0.1%
8.31426312
0.1%
5.75908710
0.1%
4.969089
0.1%
4.52470214
0.1%
4.4629835
 
< 0.1%
3.7223529
0.1%
3.26562912
0.1%

upper_margin
Real number (ℝ)

HIGH CORRELATION

Distinct204
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.003395689758
Minimum-2.426761
Maximum19.470188
Zeros0
Zeros (%)0.0%
Negative5940
Negative (%)56.9%
Memory size81.7 KiB
2022-09-02T21:21:54.125489image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-2.426761
5-th percentile-0.613138
Q1-0.259834
median-0.063555
Q30.203385
95-th percentile0.572391
Maximum19.470188
Range21.896949
Interquartile range (IQR)0.463219

Descriptive statistics

Standard deviation0.9552571162
Coefficient of variation (CV)281.314603
Kurtosis170.3014356
Mean0.003395689758
Median Absolute Deviation (MAD)0.227684
Skewness10.54813658
Sum35.440814
Variance0.9125161581
MonotonicityNot monotonic
2022-09-02T21:21:54.289599image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.189174201
 
1.9%
-0.291239175
 
1.7%
-0.220579159
 
1.5%
-0.338346152
 
1.5%
0.289748147
 
1.4%
-0.087108146
 
1.4%
-0.134216137
 
1.3%
-0.063555135
 
1.3%
0.014957134
 
1.3%
-0.09496133
 
1.3%
Other values (194)8918
85.4%
ValueCountFrequency (%)
-2.426761106
1.0%
-2.39535613
 
0.1%
-2.0891613
 
0.1%
-1.96354117
 
0.2%
-1.94783916
 
0.2%
-1.91643412
 
0.1%
-1.68089914
 
0.1%
-1.6494946
 
0.1%
-1.30404218
 
0.2%
-1.29619114
 
0.1%
ValueCountFrequency (%)
19.4701884
 
< 0.1%
17.5702024
 
< 0.1%
16.9656622
 
< 0.1%
12.6553627
 
0.1%
10.653314
 
< 0.1%
9.6562110
0.1%
7.29300418
0.2%
4.83558314
0.1%
3.8463345
 
< 0.1%
2.8806396
 
0.1%

lower_margin
Real number (ℝ)

HIGH CORRELATION

Distinct230
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00518116202
Minimum-3.210528
Maximum7.458681
Zeros0
Zeros (%)0.0%
Negative1700
Negative (%)16.3%
Memory size81.7 KiB
2022-09-02T21:21:54.474610image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-3.210528
5-th percentile-3.210528
Q10.064919
median0.217845
Q30.356544
95-th percentile0.530808
Maximum7.458681
Range10.669209
Interquartile range (IQR)0.291625

Descriptive statistics

Standard deviation0.9924296863
Coefficient of variation (CV)191.5457734
Kurtosis10.59083647
Mean0.00518116202
Median Absolute Deviation (MAD)0.142256
Skewness-1.387287093
Sum54.075788
Variance0.9849166822
MonotonicityNot monotonic
2022-09-02T21:21:54.672650image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-3.210528610
 
5.8%
0.239183148
 
1.4%
0.388552148
 
1.4%
0.235627124
 
1.2%
0.214288124
 
1.2%
0.171611119
 
1.1%
0.349432118
 
1.1%
0.299642116
 
1.1%
0.107596115
 
1.1%
0.324537114
 
1.1%
Other values (220)8701
83.4%
ValueCountFrequency (%)
-3.210528610
5.8%
-3.20697119
 
0.2%
-3.20341527
 
0.3%
-3.07538516
 
0.2%
-2.9758056
 
0.1%
-2.95802314
 
0.1%
-2.90823414
 
0.1%
-2.4814657
 
0.1%
-2.34987812
 
0.1%
-2.32498311
 
0.1%
ValueCountFrequency (%)
7.4586813
 
< 0.1%
7.4195617
0.1%
6.2601736
0.1%
5.491998
0.1%
5.1968094
 
< 0.1%
5.08300410
0.1%
4.3290475
< 0.1%
3.9413996
0.1%
3.287028
0.1%
2.486832
 
< 0.1%

exploitation
Real number (ℝ)

Distinct748
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.002615837597
Minimum-5.440122
Maximum3.987152
Zeros0
Zeros (%)0.0%
Negative4871
Negative (%)46.7%
Memory size81.7 KiB
2022-09-02T21:21:54.845432image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-5.440122
5-th percentile-1.790689
Q1-0.526838
median0.087408
Q30.627208
95-th percentile1.388496
Maximum3.987152
Range9.427274
Interquartile range (IQR)1.154046

Descriptive statistics

Standard deviation0.9914428365
Coefficient of variation (CV)379.0154395
Kurtosis3.220171595
Mean0.002615837597
Median Absolute Deviation (MAD)0.581006
Skewness-0.8334254482
Sum27.301497
Variance0.982958898
MonotonicityNot monotonic
2022-09-02T21:21:55.055269image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5.44012237
 
0.4%
-0.18441733
 
0.3%
-0.52725630
 
0.3%
0.55711822
 
0.2%
0.06264222
 
0.2%
1.19997922
 
0.2%
0.29642322
 
0.2%
0.1404922
 
0.2%
0.55789422
 
0.2%
-0.75551722
 
0.2%
Other values (738)10183
97.6%
ValueCountFrequency (%)
-5.44012237
0.4%
-3.4418374
 
< 0.1%
-3.01885312
 
0.1%
-2.98636
 
0.1%
-2.9639515
 
< 0.1%
-2.9413648
 
0.1%
-2.8322469
 
0.1%
-2.8320372
 
< 0.1%
-2.8098085
 
< 0.1%
-2.7012279
 
0.1%
ValueCountFrequency (%)
3.98715215
0.1%
2.79139214
0.1%
2.25863318
0.2%
2.21119118
0.2%
2.12358615
0.1%
2.04633616
0.2%
2.0418916
0.2%
2.0214517
 
0.1%
2.00405515
0.1%
1.9930157
 
0.1%

row_number
Real number (ℝ)

HIGH CORRELATION

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.006365146306
Minimum-4.922215
Maximum1.066121
Zeros0
Zeros (%)0.0%
Negative1585
Negative (%)15.2%
Memory size81.7 KiB
2022-09-02T21:21:55.254590image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-4.922215
5-th percentile-1.436467
Q10.17234
median0.261718
Q30.261718
95-th percentile0.976743
Maximum1.066121
Range5.988336
Interquartile range (IQR)0.089378

Descriptive statistics

Standard deviation1.007875799
Coefficient of variation (CV)-158.3429116
Kurtosis13.99927347
Mean-0.006365146306
Median Absolute Deviation (MAD)0.089378
Skewness-3.609422025
Sum-66.433032
Variance1.015813626
MonotonicityNot monotonic
2022-09-02T21:21:55.483134image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0.2617185110
49.0%
0.172341966
 
18.8%
0.976743571
 
5.5%
0.082961515
 
4.9%
-4.922215278
 
2.7%
0.351096179
 
1.7%
-0.006417144
 
1.4%
-1.078955123
 
1.2%
0.887365119
 
1.1%
-1.257711106
 
1.0%
Other values (37)1326
 
12.7%
ValueCountFrequency (%)
-4.922215278
2.7%
-4.8328373
 
< 0.1%
-4.7434595
 
< 0.1%
-4.6540816
 
0.1%
-3.8496779
 
0.1%
-3.31340810
 
0.1%
-3.2240318
 
0.2%
-3.13465212
 
0.1%
-3.04527425
 
0.2%
-2.7771397
 
0.1%
ValueCountFrequency (%)
1.06612127
 
0.3%
0.976743571
 
5.5%
0.887365119
 
1.1%
0.797987103
 
1.0%
0.70860995
 
0.9%
0.6192378
 
0.7%
0.52985227
 
0.3%
0.44047462
 
0.6%
0.351096179
 
1.7%
0.2617185110
49.0%

modular_ratio
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct234
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.008885775031
Minimum-7.450257
Maximum12.315569
Zeros0
Zeros (%)0.0%
Negative5493
Negative (%)52.6%
Memory size81.7 KiB
2022-09-02T21:21:55.814316image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-7.450257
5-th percentile-1.429155
Q1-0.598658
median-0.058835
Q30.564038
95-th percentile1.643684
Maximum12.315569
Range19.765826
Interquartile range (IQR)1.162696

Descriptive statistics

Standard deviation1.000360272
Coefficient of variation (CV)-112.5799684
Kurtosis4.823981764
Mean-0.008885775031
Median Absolute Deviation (MAD)0.581347
Skewness0.3201263528
Sum-92.740834
Variance1.000720675
MonotonicityNot monotonic
2022-09-02T21:21:56.199544image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.107265217
 
2.1%
-0.307984214
 
2.1%
-0.474083211
 
2.0%
-0.058835211
 
2.0%
0.024215208
 
2.0%
-0.349509204
 
2.0%
-0.266459203
 
1.9%
0.190314203
 
1.9%
-0.10036203
 
1.9%
-0.01731198
 
1.9%
Other values (224)8365
80.1%
ValueCountFrequency (%)
-7.4502573
< 0.1%
-4.6680921
 
< 0.1%
-4.1697951
 
< 0.1%
-4.128272
< 0.1%
-4.0036951
 
< 0.1%
-3.879121
 
< 0.1%
-3.8375951
 
< 0.1%
-3.7960712
< 0.1%
-3.7545463
< 0.1%
-3.7130211
 
< 0.1%
ValueCountFrequency (%)
12.3155691
< 0.1%
7.9969851
< 0.1%
5.0487211
< 0.1%
4.9656721
< 0.1%
4.9241471
< 0.1%
4.6334731
< 0.1%
4.5504231
< 0.1%
4.5088981
< 0.1%
4.3843241
< 0.1%
4.3427992
< 0.1%

interlinear_spacing
Real number (ℝ)

HIGH CORRELATION

Distinct229
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.002350016097
Minimum-11.935457
Maximum4.901228
Zeros0
Zeros (%)0.0%
Negative3101
Negative (%)29.7%
Memory size81.7 KiB
2022-09-02T21:21:56.410193image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-11.935457
5-th percentile-1.554093
Q1-0.044076
median0.220177
Q30.446679
95-th percentile0.824183
Maximum4.901228
Range16.836685
Interquartile range (IQR)0.490755

Descriptive statistics

Standard deviation0.9668267617
Coefficient of variation (CV)411.4128253
Kurtosis34.62247159
Mean0.002350016097
Median Absolute Deviation (MAD)0.226503
Skewness-4.213841695
Sum24.527118
Variance0.9347539871
MonotonicityNot monotonic
2022-09-02T21:21:56.607305image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.144676483
 
4.6%
0.295677464
 
4.4%
0.182426463
 
4.4%
0.220177463
 
4.4%
0.257927450
 
4.3%
0.333428434
 
4.2%
0.371178425
 
4.1%
0.446679412
 
3.9%
0.106925403
 
3.9%
0.069175373
 
3.6%
Other values (219)6067
58.1%
ValueCountFrequency (%)
-11.93545710
0.1%
-9.0664251
 
< 0.1%
-8.9909253
 
< 0.1%
-8.8776731
 
< 0.1%
-8.8021731
 
< 0.1%
-8.7266722
 
< 0.1%
-8.3114181
 
< 0.1%
-8.2736671
 
< 0.1%
-7.9716631
 
< 0.1%
-7.9087461
 
< 0.1%
ValueCountFrequency (%)
4.9012281
 
< 0.1%
4.183971
 
< 0.1%
3.9952181
 
< 0.1%
3.4667121
 
< 0.1%
3.240211
 
< 0.1%
3.1647092
< 0.1%
2.9382061
 
< 0.1%
2.9004561
 
< 0.1%
2.8627064
< 0.1%
2.7872051
 
< 0.1%

weight
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10103
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.0102593157
Minimum-4.090167
Maximum4.580832
Zeros0
Zeros (%)0.0%
Negative4709
Negative (%)45.1%
Memory size81.7 KiB
2022-09-02T21:21:56.804546image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-4.090167
5-th percentile-1.8542382
Q1-0.547709
median0.103541
Q30.639426
95-th percentile1.3946236
Maximum4.580832
Range8.670999
Interquartile range (IQR)1.187135

Descriptive statistics

Standard deviation0.9964310194
Coefficient of variation (CV)-97.12451085
Kurtosis1.187849053
Mean-0.0102593157
Median Absolute Deviation (MAD)0.584753
Skewness-0.6359868806
Sum-107.076478
Variance0.9928747765
MonotonicityNot monotonic
2022-09-02T21:21:56.992590image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6329413
 
< 0.1%
0.3031323
 
< 0.1%
0.5455533
 
< 0.1%
-0.4307313
 
< 0.1%
-0.2254923
 
< 0.1%
-0.2568083
 
< 0.1%
0.3058312
 
< 0.1%
-0.7134782
 
< 0.1%
0.6111572
 
< 0.1%
-0.061472
 
< 0.1%
Other values (10093)10411
99.8%
ValueCountFrequency (%)
-4.0901671
< 0.1%
-3.979151
< 0.1%
-3.9296011
< 0.1%
-3.9158361
< 0.1%
-3.9032741
< 0.1%
-3.8827521
< 0.1%
-3.8618611
< 0.1%
-3.8185451
< 0.1%
-3.8140991
< 0.1%
-3.7753461
< 0.1%
ValueCountFrequency (%)
4.5808321
< 0.1%
4.2511971
< 0.1%
3.8459581
< 0.1%
3.8033991
< 0.1%
3.8015361
< 0.1%
3.7881581
< 0.1%
3.3749991
< 0.1%
3.1742241
< 0.1%
3.1344421
< 0.1%
3.1335871
< 0.1%

peak_number
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct258
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.008690807608
Minimum-4.737863
Maximum3.213413
Zeros0
Zeros (%)0.0%
Negative4687
Negative (%)44.9%
Memory size81.7 KiB
2022-09-02T21:21:57.182545image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-4.737863
5-th percentile-1.993893
Q1-0.372457
median0.064084
Q30.500624
95-th percentile1.560794
Maximum3.213413
Range7.951276
Interquartile range (IQR)0.873081

Descriptive statistics

Standard deviation1.001239548
Coefficient of variation (CV)-115.2067326
Kurtosis2.371452235
Mean-0.008690807608
Median Absolute Deviation (MAD)0.43654
Skewness-0.8400208036
Sum-90.705959
Variance1.002480633
MonotonicityNot monotonic
2022-09-02T21:21:57.398571image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.064084245
 
2.3%
0.001721239
 
2.3%
-0.060642238
 
2.3%
-0.123005233
 
2.2%
0.126447228
 
2.2%
-0.029461222
 
2.1%
0.095265214
 
2.1%
0.18881211
 
2.0%
0.157628205
 
2.0%
-0.154186204
 
2.0%
Other values (248)8198
78.5%
ValueCountFrequency (%)
-4.7378633
< 0.1%
-4.6131371
 
< 0.1%
-4.4260481
 
< 0.1%
-4.3948661
 
< 0.1%
-4.2389592
< 0.1%
-4.1454151
 
< 0.1%
-4.051872
< 0.1%
-4.0206891
 
< 0.1%
-3.9895071
 
< 0.1%
-3.9583261
 
< 0.1%
ValueCountFrequency (%)
3.2134131
 
< 0.1%
3.1198681
 
< 0.1%
2.9327791
 
< 0.1%
2.9015981
 
< 0.1%
2.8392353
< 0.1%
2.8080533
< 0.1%
2.7768723
< 0.1%
2.745691
 
< 0.1%
2.7145093
< 0.1%
2.6833273
< 0.1%

ratio
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct9947
Distinct (%)95.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.0006784520456
Minimum-6.719324
Maximum11.911338
Zeros0
Zeros (%)0.0%
Negative5324
Negative (%)51.0%
Memory size81.7 KiB
2022-09-02T21:21:57.639302image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-6.719324
5-th percentile-1.3770446
Q1-0.514199
median-0.020397
Q30.526304
95-th percentile1.5429124
Maximum11.911338
Range18.630662
Interquartile range (IQR)1.040503

Descriptive statistics

Standard deviation0.9929277614
Coefficient of variation (CV)-1463.519445
Kurtosis7.526004847
Mean-0.0006784520456
Median Absolute Deviation (MAD)0.518129
Skewness-0.3967824436
Sum-7.081004
Variance0.9859055394
MonotonicityNot monotonic
2022-09-02T21:21:57.860666image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.69175912
 
0.1%
-6.71932411
 
0.1%
-0.0381667
 
0.1%
-0.5547656
 
0.1%
-0.2507226
 
0.1%
-0.1894585
 
< 0.1%
1.3174335
 
< 0.1%
-0.3977345
 
< 0.1%
0.6880485
 
< 0.1%
0.1796945
 
< 0.1%
Other values (9937)10370
99.4%
ValueCountFrequency (%)
-6.71932411
0.1%
-5.8692811
 
< 0.1%
-5.8306441
 
< 0.1%
-5.8115611
 
< 0.1%
-5.7974561
 
< 0.1%
-5.7533711
 
< 0.1%
-5.3426521
 
< 0.1%
-5.2917441
 
< 0.1%
-5.2590381
 
< 0.1%
-5.2124331
 
< 0.1%
ValueCountFrequency (%)
11.9113381
< 0.1%
7.9007291
< 0.1%
7.6541041
< 0.1%
4.3913961
< 0.1%
4.3820161
< 0.1%
4.0840381
< 0.1%
4.0522971
< 0.1%
4.0133471
< 0.1%
3.9640411
< 0.1%
3.8963881
< 0.1%

class
Categorical

HIGH CORRELATION

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size591.3 KiB
A
4286 
F
1962 
E
1095 
I
832 
X
522 
Other values (7)
1740 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10437
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowW
2nd rowA
3rd rowI
4th rowE
5th rowA

Common Values

ValueCountFrequency (%)
A4286
41.1%
F1962
18.8%
E1095
 
10.5%
I832
 
8.0%
X522
 
5.0%
H520
 
5.0%
G447
 
4.3%
D353
 
3.4%
Y267
 
2.6%
C103
 
1.0%
Other values (2)50
 
0.5%

Length

2022-09-02T21:21:58.126668image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a4286
41.1%
f1962
18.8%
e1095
 
10.5%
i832
 
8.0%
x522
 
5.0%
h520
 
5.0%
g447
 
4.3%
d353
 
3.4%
y267
 
2.6%
c103
 
1.0%
Other values (2)50
 
0.5%

Most occurring characters

ValueCountFrequency (%)
A4286
41.1%
F1962
18.8%
E1095
 
10.5%
I832
 
8.0%
X522
 
5.0%
H520
 
5.0%
G447
 
4.3%
D353
 
3.4%
Y267
 
2.6%
C103
 
1.0%
Other values (2)50
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter10437
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A4286
41.1%
F1962
18.8%
E1095
 
10.5%
I832
 
8.0%
X522
 
5.0%
H520
 
5.0%
G447
 
4.3%
D353
 
3.4%
Y267
 
2.6%
C103
 
1.0%
Other values (2)50
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Latin10437
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A4286
41.1%
F1962
18.8%
E1095
 
10.5%
I832
 
8.0%
X522
 
5.0%
H520
 
5.0%
G447
 
4.3%
D353
 
3.4%
Y267
 
2.6%
C103
 
1.0%
Other values (2)50
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII10437
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A4286
41.1%
F1962
18.8%
E1095
 
10.5%
I832
 
8.0%
X522
 
5.0%
H520
 
5.0%
G447
 
4.3%
D353
 
3.4%
Y267
 
2.6%
C103
 
1.0%
Other values (2)50
 
0.5%

Interactions

2022-09-02T21:21:50.995085image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:35.661377image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:37.431515image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:39.111434image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:40.685849image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:42.278877image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:44.031209image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:45.684906image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:47.510868image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:49.357507image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:51.144519image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:35.829344image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:37.596763image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:39.270444image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:40.848223image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:42.448796image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:44.210262image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:45.846342image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:47.728433image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:49.557556image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:51.296861image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:36.000401image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:37.746946image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:39.416161image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:41.012971image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:42.609584image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:44.372355image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:46.007244image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:47.902738image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:49.707453image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:51.489489image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:36.218578image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:37.898201image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:39.563635image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:41.165716image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:42.779206image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:44.531238image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:46.161242image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:48.062824image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:49.862717image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:51.657495image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:36.429394image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:38.050411image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:39.714301image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:41.312586image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:43.040997image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:44.694988image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:46.318662image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:48.220822image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:50.028354image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:51.818495image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:36.605783image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:38.199763image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:39.866275image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:41.464272image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:43.219568image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:44.857517image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:46.478787image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:48.380821image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:50.177540image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:52.159480image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:36.779787image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:38.365651image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:40.029389image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:41.627370image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:43.382077image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:45.026129image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:46.663713image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:48.548820image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:50.337562image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:52.325494image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:36.947464image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:38.642028image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:40.187771image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:41.795383image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:43.543886image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:45.190291image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:46.836699image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:48.710504image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:50.495686image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:52.488489image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:37.100685image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:38.793406image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:40.332722image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:41.949585image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:43.696923image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:45.345068image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:47.008044image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:48.857505image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:50.649633image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:52.693448image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:37.264437image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:38.951583image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:40.515928image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:42.109741image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:43.871644image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:45.512843image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:47.178951image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:49.138508image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-02T21:21:50.814818image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-09-02T21:21:58.330664image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-02T21:21:58.556714image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-02T21:21:58.768664image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-02T21:21:59.035666image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-09-02T21:21:53.099447image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-09-02T21:21:53.434445image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

intercolumnar_distanceupper_marginlower_marginexploitationrow_numbermodular_ratiointerlinear_spacingweightpeak_numberratioclass
0-3.4987990.2504920.2320701.224178-4.9222151.1453860.182426-0.165983-0.1230051.087144W
10.204355-0.3540490.3209800.410166-0.989576-2.2181270.2201770.1818442.090879-2.009758A
20.759828-1.304042-0.023991-0.973663-0.006417-0.349509-0.421580-0.4501270.4694430.060952I
3-0.0054900.3604090.281860-0.213479-1.168333-1.013906-0.3460801.1761650.968347-0.627999E
40.0809160.1013200.1040400.1404900.2617180.4809880.710932-0.253430-0.4971830.155681A
50.068573-0.181323-3.210528-0.294311-1.1683330.356414-0.006326-0.2195500.1264470.448186F
6-0.301743-0.3147930.3992210.7705200.7086090.564038-1.403091-1.459107-0.0918231.627420Y
70.031541-0.1185130.374326-0.0667060.2617180.6055630.559930-0.2581290.0952650.344766A
8-0.091897-0.1185130.1893931.2803030.2617180.3148890.0691751.2771830.5318060.359002A
90.3771690.0149570.3814390.2927530.261718-0.3079840.5221800.3709890.562987-0.440132H

Last rows

intercolumnar_distanceupper_marginlower_marginexploitationrow_numbermodular_ratiointerlinear_spacingweightpeak_numberratioclass
104270.105604-0.1499180.4383420.9346590.2617180.2733640.1824260.7227180.1264470.263718A
10428-0.190648-1.0920600.4027780.7494250.1723400.7716620.5599300.1817270.4070800.492670D
10429-0.0178340.234790-0.1804720.6210320.2617180.4394630.673182-0.2629820.0640840.140600F
104300.661077-0.0949600.0151300.7519610.976743-0.100360-1.4030911.0370541.4360690.926642I
10431-0.4375250.4232180.3885520.6208520.172340-0.8893320.1446762.1310341.373706-0.771516E
10432-0.128929-0.0400010.0578070.5578940.261718-0.930856-0.0440761.1584582.277968-0.699884X
104330.2660740.556689-0.0204340.1766240.261718-0.5156080.5976810.1783490.625350-0.657245G
10434-0.0548660.5802420.032912-0.0166680.2617181.5191090.371178-0.985508-0.4036381.276301A
104350.0809160.5880930.0151300.0022500.261718-0.930856-0.2705790.163807-0.091823-0.593329F
104360.3771690.0149570.3814390.2927530.261718-1.470679-0.006326-0.494919-0.247731-1.212974H